Edge AI Acceleration with sensAI

Industrial Automation,Smart Home,Automotive,Consumer Electronics Application

Description

Implement efficient AI/ML inference on Lattice FPGAs using the sensAI solution stack. Achieve 10x lower power consumption compared to traditional processors while maintaining real-time performance for vision, audio, and sensor fusion applications.

Core Advantages

Ultra-Low Power sensAI enables AI inference with sub-1W power consumption, making it possible to deploy AI in battery-powered devices and thermally constrained environments where traditional processors would consume too much power.

Flexible Architecture Support for various neural network architectures including CNNs for vision, RNNs for sequence processing, and custom models for specialized applications provides flexibility for diverse AI workloads.

Deterministic Performance FPGA-based implementation provides deterministic latency for time-critical AI applications, unlike processor-based solutions with variable execution times.

Recommended Bill of Materials (BOM)

Item	Part Number	Description	Quantity	Datasheet
1	Lattice FPGA (LFD2NX-40/LFD4NX-100/LCE4X-100)	Main processing device	1	📄 Download
2	DDR3/DDR4 Memory	External memory for data buffering	1-2	📄 Download
3	Power Management IC	Voltage regulation for FPGA	1	📄 Download
4	MIPI Camera Module (optional)	Image sensor for vision applications	1-4	📄 Download

Applications

Object detection and classification

Anomaly detection in manufacturing

Voice recognition and keyword spotting

Predictive maintenance

Face detection and recognition

Technical Specifications

Supported Frameworks

TensorFlow, PyTorch, ONNX, Caffe

Quantization

8-bit weights and activations

Max Power

<1W typical

Latency

Deterministic, framework dependent

Development Tool

sensAI Studio

Customer Success Stories

Industrial Equipment Manufacturer

| Predictive maintenance with vibration analysis

Challenge

Need AI inference in harsh industrial environment with limited power

Solution

Implemented sensAI on LFD2NX-40 for real-time anomaly detection

Results

Achieved 95% detection accuracy with 200mW power consumption, enabling battery-powered wireless sensors

Smart Home Device Maker

| Voice recognition and keyword spotting

Challenge

Always-on voice processing with strict power budget

Solution

Deployed RNN-based keyword spotting using sensAI on LFD2NX-40

Results

Sub-100mW always-on operation with 98% keyword recognition accuracy

FAE Expert Insights

Senior FAE

Applications Engineer

10+ years

Professional Insights

Having supported numerous sensAI deployments over the past 5 years, I've seen the transformative impact of ultra-low power AI at the edge. The key insight is that sensAI enables AI in places where it was previously impossible due to power constraints. Customers are often surprised that they can achieve 30 FPS object detection on a battery-powered camera consuming less than 200mW. The most successful implementations start with reference designs and gradually customize. Common mistake: trying to deploy unoptimized models without quantization - always use sensAI Studio for proper optimization. For best results, engage with our FAE team early to validate model compatibility and performance targets.

Key Takeaways

sensAI enables AI in power-constrained applications
Always quantize models to 8-bit for optimal performance
Start with reference designs and customize gradually
Engage FAE early for model validation

Decision Framework

sensAI Selection Framework

Steps:

Verify model fits FPGA resources
Quantize and optimize with sensAI Studio
Validate performance on development kit

Ready to Implement This Solution?

Contact our FAE team for design support and quotes

Frequently Asked Questions

What neural network architectures are supported by sensAI?

sensAI supports Convolutional Neural Networks (CNNs) for image classification and object detection, Recurrent Neural Networks (RNNs) for sequence processing and voice recognition, and custom architectures for specialized applications. The neural network compiler optimizes these models for FPGA implementation with 8-bit quantization.

How do I convert my trained model for sensAI?

Use sensAI Studio to import your trained model from TensorFlow, PyTorch, or ONNX format. The tool automatically quantizes weights and activations to 8-bit, optimizes the model for FPGA implementation, and generates the FPGA bitstream. The process typically takes minutes to hours depending on model complexity.

What performance can I expect from sensAI implementations?

Performance depends on the specific model and FPGA device. Typical results include MobileNet v2 at 30 FPS on LFD2NX-40 consuming 150mW, ResNet-18 at 15 FPS on LFD4NX-100 consuming 280mW, and Tiny YOLO at 25 FPS on LFD4NX-100 consuming 320mW. These is 10x lower power than equivalent GPU implementations.

Do I need FPGA expertise to use sensAI?

Basic FPGA knowledge is helpful but not required. sensAI Studio provides an automated workflow that handles most FPGA implementation details. Reference designs demonstrate typical applications. Our FAE team can provide guidance for custom implementations and optimization.

What development hardware do I need?

A Lattice development kit with the target FPGA (LFD2NX-40, LFD4NX-100, or LCE4X-100) is recommended for development and evaluation. sensAI Studio software is available as a free download. Example designs and documentation are included to accelerate development.